CoVA or Context-Aware Visual Attention-based end-to-end pipeline for Webpage Object Detection is a technology that aims to predict labels for a webpage containing various elements. This prediction is made by learning function f.
What Does CoVA Consist Of?
CoVA receives three inputs: a screenshot of a webpage, a list of bounding boxes, and neighborhood information for each element obtained from the DOM tree.
The technology uses four stages to process this information:
Stage 1: Graph Representation Extraction For The Webpage
The first stage involves the computation of every web element's set of neighboring web elements. This computation is used by the following stages to make predictions.
Stage 2: Representation Network (RN)
The Representation Network or RN consists of a Convolutional Neural Net and a positional encoder that learn a visual representation for every web element.
Stage 3: Graph Attention Network (GAT)
The Graph Attention Network or GAT combines the visual representation of a web element that is to be classified and its neighbors to compute its contextual representation. The GAT is an important stage that significantly improves the accuracy of predictions.
Stage 4: Fully Connected Layer (FC)
Finally, the visual and contextual representations of the web element are concatenated and passed through the FC layer to obtain the classification output.
CoVA's Importance
CoVA is important because it can help detect malicious web elements on a webpage, such as fake login forms or phishing links, as well as automate tasks like data extraction or object recognition.
Moreover, CoVA can improve webpage accessibility by identifying webpage elements like images, buttons, and text areas, and describing them to individuals with visual impairments.
CoVA is a powerful technology that can predict the labels of the various elements of a webpage. It is important for detecting malicious web elements and can also help automate tasks and improve webpage accessibility. The four stages of CoVA's processing make it effective in achieving its goal of predicting labels with accuracy.
